Title :
Scaling Transform Method for Remotely Sensed FAPAR Based on FAPAR-P Model
Author :
Lu Wang ; Wenjie Fan ; Xiru Xu ; Yuan Liu
Author_Institution :
Beijing Key Lab. of Spatial Inf. Integration & Its Applic., Peking Univ., Beijing, China
Abstract :
Climate and land-atmosphere models rely on accurate land-surface parameters, such as the fraction of absorbed photosynthetically active radiation (FAPAR). It is known that FAPAR values retrieved from remote-sensing images suffer from scaling effects. Scaling transformation aims to derive accurate FAPAR values at a specific scale from values at other scales. In this letter, the scaling-effect mechanism and the scale-transformation algorithm are derived using a Taylor series expansion method based on the FAPAR model based on P after simplification. The scaling algorithm was validated in the Heihe River Basin. The multiscale FAPAR values are inverted from 5-, 50-, and 100-m hyperspectral reflectance data. The scale-transformation formula was used, and the results agreed well with actual values.
Keywords :
atmospheric radiation; geophysical techniques; hyperspectral imaging; remote sensing; series (mathematics); sunlight; transforms; vegetation; China; FAPAR values; FAPAR-P Model; Heihe River Basin; Taylor series expansion method; climate model; fraction of absorbed photosynthetically active radiation; hyperspectral reflectance data; land-atmosphere model; land-surface parameters; multiscale FAPAR value inversion; remote-sensing images; remotely sensed FAPAR; scale-transformation algorithm; scaling effect; scaling transform method; scaling-effect mechanism; solar radiation; vegetation; Equations; Indexes; Mathematical model; Remote sensing; Soil; Transforms; Vegetation mapping; Effective leaf area index (LAI); FAPAR model based on $P$ (FAPAR-P model); fraction of absorbed photosynthetically active radiation (FAPAR); scaling transformation;
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
DOI :
10.1109/LGRS.2014.2359051